Relocation Module and Methods for Surgical Equipment

This application is a continuation of U.S. application Ser. No. 19/077,404, filed Mar. 12, 2025, which is a continuation of U.S. application Ser. No. 18/943,055, filed Nov. 11, 2024, now issued as U.S. Pat. No. 12,279,994, which is a continuation of U.S. application Ser. No. 18/598,110, filed Mar. 7, 2024, now issued as U.S. Pat. No. 12,178,759, which is a continuation of U.S. application Ser. No. 18/142,787, filed May 3, 2023, now issued as U.S. Pat. No. 12,023,281, which is a continuation of U.S. application Ser. No. 18/099,074, filed Jan. 19, 2023, which is a continuation of U.S. application Ser. No. 17/874,963, filed Jul. 27, 2022, now issued as U.S. Pat. No. 11,648,166, which is a continuation of U.S. application Ser. No. 17/873,857, filed Jul. 26, 2022, now U.S. Pat. No. 11,654,070, which is a continuation of U.S. application Ser. No. 17/528,832 filed Nov. 17, 2021, now issued as U.S. Pat. No. 11,432,982, which is a continuation-in-part of U.S. application Ser. No. 17/376,469 filed Jul. 15, 2021, now issued as U.S. Pat. No. 11,219,570, which is a continuation-in-part of U.S. application Ser. No. 17/199,722 filed Mar. 12, 2021, now issued as U.S. Pat. No. 11,173,089, which is a continuation of U.S. application Ser. No. 17/092,681, filed Nov. 9, 2020, now issued as U.S. Pat. No. 10,993,865, which is a continuation of U.S. application Ser. No. 16/879,406, filed May 20, 2020, now issued as U.S. Pat. No. 10,869,800, which is a continuation-in-part of U.S. application Ser. No. 16/601,924, filed Oct. 15, 2019, now issued as U.S. Pat. No. 10,702,436, which is a continuation of U.S. application Ser. No. 16/593,033, filed Oct. 4, 2019, now issued as U.S. Pat. No. 10,653,577, which is a continuation of U.S. application Ser. No. 16/364,884, filed Mar. 26, 2019, now issued as U.S. Pat. No. 10,507,153, which claims the benefit of priority to U.S. Provisional Patent Application 62/782,901, filed Dec. 20, 2018. U.S. application Ser. No. 16/364,884, filed Mar. 26, 2019, now issued as U.S. Pat. No. 10,507,153 is also a continuation-in-part of U.S. application Ser. No. 15/935,524, filed Mar. 26, 2018, now issued as U.S. Pat. No. 10,512,191.

U.S. application Ser. No. 17/376,469, now U.S. Pat. No. 11,219,570, is also a continuation-in-part of U.S. application Ser. No. 17/245,942, filed Apr. 30, 2021, now U.S. Pat. No. 11,426,318, which is a continuation-in-part of U.S. application Ser. No. 17/167,681, filed Feb. 4, 2021, now issued as U.S. Pat. No. 11,160,710, which is a continuation-in-part of U.S. application Ser. No. 17/092,681, filed Nov. 9 2020, now issued as U.S. Pat. No. 10,993,865, which is a continuation of U.S. application Ser. No. 16/879,406, filed May 20, 2020, now issued as U.S. Pat. No. 10,869,800.

The disclosures of each of these applications is incorporated herein by reference in its entirety.

This document pertains generally, but not by way of limitation, to systems and methods for improving safety in operating rooms and hospitals. In particular, the systems and methods described herein may include but are not limited to, anesthetic, surgical and medical equipment storage and operational data capture, automated anesthetic and patient monitoring data capture and electronic record input.

Anesthesia monitors and equipment as well as surgical equipment have been invented, developed and sporadically introduced into surgical practice over more than a century. This equipment is made by a wide variety of companies who have no incentive to coordinate with one another to create the most efficient operating room. Equipment throughout the operating room has been placed in one location or another, generally without a plan and then decades later, is still sitting in that unplanned location.

Over the past 20 years, there has been a gradual movement to replacing paper anesthetic records with electronic anesthetic records (EAR). The digital electronic data outputs of the patient's physiologic monitors have been relatively easy to input into the EAR. However, the identity, dosing and timing of IV and inhaled drug administration, IV fluid administration, oxygen and ventilation gas administration and anesthetic events such as intubation have required manual input to the EAR by way of a computer keyboard and mouse. Blood, fluid and urine outputs have also required manual input to the EAR by way of a computer keyboard and mouse. The surgical equipment scattered around the operating room either does not produce a digital output that could memorialize the equipment's operation to the electronic record, that output is not automatically captured, or the output is not provided in a way that provides meaningful context.

Carefully observing the patient in various conditions and situations including surgery has been an important source medical information for centuries. However, in this age of electronic monitoring, patient observation by the healthcare provider is becoming a lost art that is infrequently done and if it is done it may not be entered into the record so the information is lost.

Most electronic medical equipment, especially equipment constituting dose events, do not produce an electronic data record documenting the equipment's operating parameters. A complete electronic health record (EHR) requires instant documentation of all dose events in order to allow meaningful clinical decision support (CDS) and AI analysis of dose and response events.

The concept of “garbage in, garbage out” is of the utmost importance for medical algorithms trained on healthcare datasets. The majority of data categories in the acute care setting are manually inputted, basically a digitized paper record. Manually inputted data is sporadic and prone to errors and omissions. It is also not time-stamped for temporal correlations. The result is that when those data are aggregated into a “big data” database, the data is incomplete, inconsistent, missing and often unusable. Further, clinicians hate the data entry part of their jobs, seeing it as time consuming and distracting from patient care. Several studies have linked manual data entry to the electronic record as a significant contributor to physician burnout.

In some examples, the automated data consolidation module of this disclosure minimizes “garbage in” of healthcare “big data” by automating the data input process.

As a result of the current medical practices described, the majority of the input to the electronic anesthetic and medical records has been the data from the vital signs monitors, recording the patient's “response.” The “dose” events (things that are given or done to the patient leading to the “response”) are manually entered into the record, resulting in mistakes, omissions and no temporal correlation between the dose and response. The incomplete and inaccurate records make any analysis with artificial intelligence and machine learning software problematic, either for that individual patient or for “big data” analysis of populations of patients.

This document pertains generally to systems and methods for improving safety for patients receiving intravenous (IV) medications and fluids, by avoiding medication or fluid errors and documenting the administration. This document pertains generally, but not by way of limitation, to systems and methods for constructing granular (beat-by-beat) anesthetic, surgical and patient records that include both “dose” events (things that are given or done to the patient) and “response” events (inputs from electronic monitors, measurement devices and machine vision “observations”). The dose and response events are precisely temporally related and recorded in the patient's electronic record and may be pooled with the records of other patients in a database that can be analyzed with artificial intelligence and machine learning software.

Illustrative examples of an automated data consolidation module that systematizes surgical safety for patients and OR personnel. In some examples, this automated data consolidation module is designed to house nearly all of the operating room patient monitors and support equipment. Even dissimilar types of equipment that are normally kept separate from one another. In some examples, this unique automated data consolidation module is specially designed to fit next to and under the arm-board of the surgical table—a location traditionally occupied by an IV pole. For the past 100 years, this location has been a wasted “no-man's land” between the anesthesia and surgical sides of the operating room. In reality, the unique space next to and under the arm-board, is truly the “prime real estate” of the entire operating room: it is immediately adjacent the patient for optimal monitoring while simultaneously maintaining observation of the patient and surgical procedure; equipment controls can be conveniently accessed by both the anesthesia and surgical staff; short cables and hoses are adequate to reach the patient; and it is uniquely accessible from both the anesthesia and surgical sides of the anesthesia screen. The unique space next to and under the arm-board is the only location in the entire operating room where cables, cords and hoses from both the anesthesia side and the sterile surgical field side, do not need to traverse the floor or even touch the floor in order to connect to their respective monitor or patient support equipment—truly a remarkable location that has been wasted by conventional systems.

In some examples, an illustrative automated data consolidation module can house both anesthesia related and non-anesthesia related equipment. In some examples, the illustrative relocation module can house a variety of non-proprietary OR equipment such as patient vital sign monitors, electro-surgical generators, anesthesia machines and mechanical ventilators. In some examples, the automated data consolidation module is designed to also house newer proprietary safety equipment such as: air-free electric patient warming, surgical smoke evacuation, waste alcohol and oxygen evacuation, evacuation of the flow-boundary dead-zones that cause disruption of the OR ventilation and the evacuation and processing of waste heat and air discharged from OR equipment. In some examples, this automated data consolidation module may also house dissimilar equipment (e.g., unrelated to anesthesia monitoring) such as: air mattress controls and air pumps; sequential compression legging controls and air pumps; capacitive coupling electrosurgical grounding; RFID counting and detection of surgical sponges; the waste blood and fluid disposal systems; and “hover” mattress inflators. Any of these devices may be stored in the automated data consolidation module together with (or without) anesthesia equipment.

In some examples, the automated data consolidation module is a specialized and optimally shaped rack for holding and protecting the patient monitors and other electronic and electromechanical surgical equipment, in a unique location. A location that is very different from just setting anesthesia monitors on top of the anesthesia machine and scattering other equipment across the floor of the operating room. The automated data consolidation module may be used anywhere throughout the hospital or long term care settings.

The various pieces of electronic and electromechanical equipment housed within the automated data consolidation module disclosed herein can produce relatively large amounts of waste heat. In some examples, the automated data consolidation module may include a waste heat management system to safely dispose of the waste heat created by the electronic and electromechanical equipment housed within the automated data consolidation module.

It would be difficult or even impossible to manage the uncontained waste heat produced by electronic and electromechanical equipment mounted on a simple open rack because it can escape in any direction. In some examples, the module can include a “cowling” covering some or all of the outer surface. The cowling not only protects the equipment from accidental fluid damage but also confines the waste heat from the electronic and electromechanical equipment mounted within the module, to the inside of the module and cowling. In some examples, the confined waste heat can then be safely managed.

In some examples, the cowling cover of the automated data consolidation module can form or support a waste heat management system. In some examples, the cowling can be provided on an inner surface of the housing. In some examples, the cowling can be described as an insulation. In some examples, the housing can include other types of insulation from heat and/or water. Any suitable type of insulated housing suitable for use in a surgical field can be provided.

In some examples, the automated data consolidation module of the instant invention may also contain the components of the anesthesia gas machine. So-called “gas machines” are relatively simple assortments of piping, valves, flow meters, vaporizers and a ventilator. These could be located within the automated data consolidation module or attached to the automated data consolidation module for further consolidation of equipment and for improved access to the patient.

In some examples, locating the anesthesia machine in or on the automated data consolidation module allows direct access for and sensors and monitors related to the anesthesia machine, to input data to the electronic anesthetic record being recorded by equipment in the automated dose/response record system.

In some examples, the collection canisters for waste fluid and blood may be conveniently mounted on the module.

In some examples, the controls and display screens for the surgical equipment housed in the automated data consolidation module may be wirelessly connected to a portable display screen such as an iPad or “smart tablet,” for convenient access by the nurse anywhere in the room. A remote display screen can also allow remote supervision and consultation.

In some examples the automated data consolidation module may be located next to the patient's bed in the ICU, ER, on the ward or in long term care. While most of the data collected by the automated data consolidation module will occur in the acute care setting, it should be understood that the automated data consolidation module concept for automatically collecting and consolidating data from a wide variety of data sources including monitors and other medical equipment, can be applied throughout the healthcare delivery system.

Doctors and nurses dislike record keeping and the switch to the electronic record has made the act of record keeping more difficult and time consuming. Entering the electronic record into the computer sometime after the event occurred and the case has settled down, is not only distracting from patient care but leads to inaccurate records. Hand entered records also bypass the opportunity for the computer to add to patient safety by checking drug identities, dosages, side effects, allergies and alerting the clinician to potential problems or even physically stopping the drug administration. Manually entered records are not useful for managing drug inventories because a given medication administration is not tied to a specific drug bottle or syringe. Finally, the computer mouse and keyboards have been shown to be contaminated by a wide variety of infective organisms and are virtually impossible to clean. Automatic anesthetic data entry to the EAR would improve patient safety, improve clinician job satisfaction and improve OR inventory management.

In general, doctors and nurses are not interested in replacing themselves and their jobs with automated drug delivery or automated anesthesia systems. However, they may be more open to automated record keeping. The challenge with automated record keeping is automating the data input that documents the numerous activities, anesthesia related events, fluid, gas and medication administration that constitute an anesthetic experience or another medical situation.

The second challenge in implementing an automated electronic anesthetic record (EAR) or automated electronic medical record (EMR) is to force as little change in routine as possible onto the anesthesiologist and other clinicians using this system. Anesthesiologists and surgeons are notoriously tradition-bound and resistant to any changes in their “tried and true” way of doing things. Therefore, a successful automated EAR must interact seamlessly with current anesthesia practices and operating room workflow without causing any disruptions.

In some examples, the automated data consolidation module of this disclosure includes a system for automatically measuring and recording the administration of IV medications and fluids. The system for automatically measuring and recording the administration of IV medications and fluids can include one or more sensors, such as one or more of a barcode reader and an RFID interrogator for accurately identifying a medication or fluid for IV administration.

In some examples, the system for automatically measuring and recording the administration of IV medications and fluids can also include one or more digital cameras with machine vision software (“machine vision”) for accurately measuring the volume of medication administered from a syringe or fluid administered from an IV bag through a drip chamber into an IV tubing. The digital cameras with machine vision software essentially duplicate the clinician's vision of an activity, injection of a drug from a syringe for example, without interfering in the normal activity and yet allows automatic recording of the activity in the EAR. The machine vision software can include one or more machine-readable mediums that when implemented on hardware processing circuitry of the system or in electrical communication with the system, can perform the functions described herein.

In some examples, the automated data consolidation module of this disclosure uses machine vision to unobtrusively “observe” the flow rate of the ventilation gas flow meters and inhaled anesthetic vaporizers.

In some examples, the automated data consolidation module of this disclosure captures input data from the blood and fluid collection and urine output collection systems of this disclosure.

In some examples, the automated data consolidation module of this disclosure lets the computer (e.g., a processor and memory for performing instructions) add to patient safety by checking drug identities, dosages, side effects, allergies, the patients' medical history and vital signs and alerting the clinician to potential problems or even physically stopping the drug administration. In some examples, the automated data consolidation module of this disclosure eliminates medication errors by checking the drug to be injected against the physician's medication orders before the injection can occur. In some examples, the automated data consolidation module of this disclosure is useful for managing drug inventories because a given medication administration is tied to a specific drug bottle or syringe.

In some examples, the automated data consolidation module of this disclosure may also automatically record and display many other functions including but not limited to: IV fluid administration, medication infusions, the patient's vital signs, urine output, blood loss, ventilator settings, inspired gases, electrosurgical settings, pneumoperitoneum insufflation settings, RFID surgical sponge counts, surgical information and video, dialysis or other medical procedure information and patient activity.

“Dose/response” is one of the most basic of all medical processes. Since the beginning of medical practice, both the art and science of medicine have relied on giving something to the patient (a medicine for example) or doing something to the patient (mechanical ventilation or surgery for example)—the “dose”, and then observing the patient's “response.” The problem now seen with electronic records is that the only data that is timely recorded is the “response” data provided by the physiologic monitors. Even that response data is frequently not recorded beat-by-beat but rather intermittently recorded every 5 minutes or 30 minutes or 4 hours for example. All of the “dose” data is entered into the electronic record by hand and therefore is prone to mistakes, omissions and unknown timing. Therefore, with current EMRs, the dose and response data cannot be temporally correlated with any accuracy, vastly reducing the analytical and predictive value of the electronic database and record.

In some examples, the automated data consolidation module of this disclosure includes systems and methods for constructing granular (beat-by-beat, second-by-second) anesthetic, surgical and patient records that include both “dose” events—the things that are given or done to the patient (inputs from medication injection and fluid monitors, various support equipment and machine vision “observations” for example) and “response” events (inputs from electronic monitors, measurement devices and machine vision “observations” for example). In some examples, the invention of this disclosure automatically enters both dose and response events into the electronic record. In some examples, the invention of this disclosure automatically enters both dose and response events into the electronic record and temporally correlates the dose and response events, such as but not limited to, when they are recorded. In some examples, the automatically entered, temporally correlated dose and response events in the patient's electronic record may be analyzed by artificial intelligence (AI) and/or machine learning (ML) software stored in a memory of a storage device electrically coupled to the processing circuitry of the module for immediate advice, alerts and feedback to the clinician. In some examples, the automatically entered, temporally correlated dose and response events in the patient's electronic record may be pooled with the records of other patients in a database that can be analyzed with artificial intelligence and machine learning software.

Machine Learning (ML) is an application that provides computer systems the ability to perform tasks, without explicitly being programmed, by making inferences based on patterns found in the analysis of data. Machine learning explores the study and construction of algorithms (e.g., tools), that may learn from existing data and make predictions about new data. Such machine-learning algorithms operate by building an ML model from example training data, in order to make data-driven predictions or decisions expressed as outputs or assessments. The principles presented herein may be applied using any suitable machine-learning tools.

In some examples, the AI or ML software can compare dose and response events from different periods of time for the same patient to learn and identify the particular patient's responses. In some examples the machine learning software can be trained to identify a patient's responses using training obtained from a plurality of patients that may include or not include the patient being monitored. Any suitable AI or ML can be implemented to interpret the data generated by the module. Current methods of obtaining data in medical settings cannot generate, store or aggregate such data for analysis using AI or ML. Thus, the dose-response systems described herein provide a technical solution to a technical problem.

Machine vision cameras and software are very good at measuring distances, movements, sizes, looking for defects, fluid levels, precise colors and many other quality measurements of manufactured products. Machine vision cameras and software can also be “taught” through AI and ML to analyze complex and rapidly evolving scenes, such as those in front of a car driving down the road.

In some examples, the automated data consolidation module of this disclosure includes novel systems and methods for using portable machine vision cameras and software to “observe” the operating parameters of various dose event and other equipment. In some examples, the automated data consolidation module of this disclosure includes novel systems and methods for using portable machine vision cameras and software to “observe” the response parameters of various response event equipment. In some examples, the portable machine vision cameras may include: laser pointers to aid in aiming and coded labels on the items or devices to be identified to aid in ML identification. In some examples, the laser pointers and coded labels help to structure the scene so that ML can be simplified.

In some examples, the automated data consolidation module of this disclosure includes novel systems and methods for using machine vision cameras and software to “observe” the patient. If the patient is in surgery, the patient's head may be the focus of the observation. In some examples, during surgery the machine vision cameras and software may be “looking” for dose events including but not limited to mask ventilation or endotracheal intubation. In some examples, during surgery the machine vision cameras and software may be “looking” for response events including but not limited to grimacing or tearing or coughing or changes in skin color.

If the patient is on the ward or in the nursing home or other long-term care facility, the whole patient may be the focus of the observation. In some examples, if the patient is on the ward or in the nursing home or other long-term care facility the machine vision cameras, processing circuitry and software may be configured to “look” for dose events (e.g., sense) including but not limited to repositioning the patient, suctioning the airway or assisting the patient out of bed or any other nursing procedure, eating and drinking. In some examples, if the patient is on the ward or in the nursing home or other long-term care facility (including at home) the machine vision cameras, processing circuitry and software may be configured to “look” for response events (e.g., sense) including but not limited to restlessness or getting out of bed without assistance or coughing or breathing pattern. In some examples, the system can go beyond traditional physiologic monitors. Even physiologic response information such as pain may be detected by facial expression analysis.

In some examples, vital signs such as heart rate, respiration rate, blood oxygen saturation and temperature can be measured (e.g., sensed, monitored) remotely via camera-based methods. Vital signs can be extracted from the optical detection of blood-induced skin color variations—remote photoplethysmography (rPPG).

In some examples, the automated data consolidation module may allow remote viewing of the displayed patient information. In some examples, the remotely displayed patient information may be used for remote medical supervision such as an anesthesiologist providing remote supervision to a nurse anesthetist who is administering the anesthetic. In some examples, the remotely displayed patient information may be used for remote medical consultation. In some examples, the remotely displayed patient information may be used to document the involvement of remote medical supervision or consultation for billing purposes.

In some examples, the automated data consolidation module allows rules to be applied to the various medical equipment that is housed within the module, mounted on the module, or is in electrical communication with or in wireless communication with the module. In some examples, the rules can include one or more of the following: that all equipment produce data reflecting the equipment's operating parameters and sensor inputs, the data is produced in prescribed data formats, the data include all prescribed input record fields for that specific type of equipment, the data is instantly and continuously provided.

In some examples, the automated data consolidation module includes processing circuitry and software that accept the data inputted from the various medical equipment. In some examples, the processing circuitry and software can translate data that is not inputted in the prescribed format. In some examples, the processing circuitry and software can add time stamps to the data to add a temporal context. In some examples, the processing circuitry and software can do data “filtering” in the presence of large size data to discard information that is not useful for healthcare monitoring based on a defined criterion. This may include for example, intermittently recording data that changes slowly such as the patient's temperature, rather than continuously recording. In some examples, the processing circuitry and software can do data “cleaning” such as normalization, noise reduction and missing data management. Sensor fusion is a technique that may be utilized to simultaneously analyze data from multiple sensors, in order to detect erroneous data from a single sensor. In some examples, the processing circuitry and software can be used in many other ways to cleanse, organize and prepare the input data.

In some examples, the processing circuitry and software execute “stream processing” for applications requiring real-time feedback. In some examples, streaming data analytics in healthcare can be defined as a systematic use of continuous waveform and related medical record information developed through applied analytical disciplines, to drive decision making for the patient care.

In some examples, when the objective is to deliver data to a “big data” database, the data must be pooled. Data in the “raw” state needs to be processed or transformed. In a service-oriented architectural approach, the data may stay raw and services are used to call, retrieve and process the data. In the data warehousing approach, data from various sources is aggregated and made ready for processing, although the data is not available in real-time. The steps of extract, transform, and load (ETL) can be used to cleans and ready data from diverse sources.

In some examples, with “big data” database data, the processing circuitry and software may execute “batch processing,” analyzing and processing the data over a specified period of time. Batch processing aims to process a high volume of data by collecting and storing batches to be analyzed in order to generate results. In some examples, the processing circuitry and software can serve as a “node” in batch computing, where big data is split into small pieces that are distributed to multiple nodes in order to obtain intermediate results. Once data processing by nodes is terminated, outcomes will be aggregated in order to generate the final results.

The following detailed description is exemplary in nature and is not intended to limit the scope, applicability, or configuration of the invention in any way. Rather, the following description provides practical illustrations for implementing exemplary examples of the present invention. Examples of constructions, materials, dimensions, and manufacturing processes are provided for selected elements, and all other elements employ that which is known to those of skill in the field of the invention. Those skilled in the art will recognize that many of the examples provided have suitable alternatives that can be utilized.

As described herein, operably coupled can include, but is not limited to, any suitable coupling, such as a fluid (e.g., liquid, gas) coupling, an electrical coupling or a mechanical coupling that enables elements described herein to be coupled to each other and/or to operate together with one another (e.g., function together).

“Dose/response” can be a useful medical tool. Dose/response involves giving something to the patient (a medicine for example) or doing something to the patient (mechanical ventilation or surgery for example)—the “dose”, and then observing the patient's “response.”